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With various kinds of intelligent metering equipment coming into service, there has been an urgent need for a set of examination system in the electric power industry to check the staff's level of measuring electric energy. In this paper, we design and implement a random test paper generation algorithm for this examination system, and analyze the experiment's data, based on the practical requirement of a training system for the electric energy measurement in a certain electric company. At the same time, in order to get a set of test papers to satisfy the given conditions, this paper discusses how to use optimized genetic algorithm to generate test papers from the question bank. This paper introduce fishnet algorithm to generate test papers automatically for getting a better, more fair and more objective test paper. [ABSTRACT FROM PUBLISHER]

ABSTRACT There have been many studies on the factors influencing paper citation counts. A number of studies have focused on the citing papers, and corresponding methods were proposed to measure the prestige of citations based on the journal impact factors, the total citation counts and the PageRank algorithm values. However, there are drawbacks to these methods. In this paper, we propose a novel method to identify prestigious citations from the affiliation of the citing paper. Specifically, if the authors of the citing paper are affiliated with a prestigious university, the citing paper could be counted as a prestigious citation. As a pilot, we used the top 200 universities on the QS World University Rankings 2015 to identify the prestigious universities so that the prestigious citations, named as QS citations, were identified accordingly. Experimental results validated that QS citations have more important impact on the cited papers than other citations. Papers with QS citations have better performance across most disciplines not only in total citation counts, but also in altmetrics such as the Altmetric Attension Score and Mendeley reader counts. [ABSTRACT FROM AUTHOR]

To deal with non-paper test composition algorithm impact on exam quality, we proposed the test-sheet composition algorithms. By comparing a variety of existing intelligent algorithms in the application of test-sheet composition, we identify the shortcomings of existing algorithms, such as the "premature" of algorithm due to the poor local search ability and the low convergence rate, etc. PSO algorithm has no crossover, mutation operators. It directly provides the speed, position update formula, and completes the assessment with the help of the fitness function of iterations. The principles and mechanisms of algorithm are simpler. On the basis of standard PSO algorithm, we proposed a Binary Particle Swarm Optimize (BPSO) algorithm based on probability. Bayes formula was used to overcome the human factors impacting on algorithm convergence speed. The algorithm validity has been shown in the simulation experiment with Java. [ABSTRACT FROM PUBLISHER]

In the current era, one of the key challenges in utilizing the Holt-Winters Method of forecasting is the accurate selection of smoothing coefficients. To address this issue, researchers have explored an optimization approach that aims to minimize forecasting errors, such as Mean Squared Errors (MSE) or Mean Absolute Deviation (MAD). This paper presents a novel methodology that employs a Genetic Algorithm (GA) to optimize the forecasting error by determining the optimal smoothing coefficients for the Holt-Winters Method. The objective value of the optimization problem is the Mean Square Error (MSE), which serves as a measure of the accuracy of the forecast. To evaluate the effectiveness of the proposed approach, actual test cases based on rice stock commercial commodity in the Philippines during the COVID-19 pandemic were utilized. The paper examines different variants of the Holt-Winters Method and assesses their suitability for capturing the characteristics of the rice stock data. The findings indicate that an additive seasonal effect is more appropriate for modeling the seasonal patterns observed in the rice stock data. Furthermore, the performance of the proposed GA-based approach is compared to other forecasting models to ascertain its efficacy. The results demonstrate promising outcomes, suggesting that the GA-based optimization approach for determining the smoothing coefficients in the Holt-Winters Method improves the accuracy of rice stock forecasting during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Selection of the most significant variables, i.e. the wavenumber, from an infrared (IR) spectrum is always difficult to be achieved. In this preliminary paper, the feasibility of genetic algorithms (GA) in identifying most informative wavenumbers from 150 IR spectra of papers was investigated. The list of selected wavenumbers was then employed in Linear Discriminant Analysis (LDA). GA procedure was repeated 30 times to get different lists of variables. Then the performances of LDA models were estimated via leave-one-out cross-validation. A total of six to eight wavenumbers were identified to be valuable variables in the GA procedures. All the 30 LDA models achieve correct classification rates between 97.3% to 100.0%. Therefore the GA-LDA model could be a suitable tool for differentiating white papers that appeared to be highly similar in their IR fingerprints. [ABSTRACT FROM AUTHOR]

The application of the Automated Storage and Retrieval System (AS/RS) in the warehouse has been improved significantly over the last few decades. In the recent years, a new type of AS/RS, Shuttle Based Storage and Retrieval System (SBS/RS) has taken many industries interest by storm due to its flexibility and throughput performances. This paper studies the mathematical optimization of item location assignment and task scheduling in SBS/RS mainly with tier-to-tier system which allows the shuttle to transport between tiers using a lift system. Since this system is mostly applied in giant e-commerce industries, considering order data set and multiple stock keeping unit (SKU) in the mathematical model formulation will give a significant benefit in the real-life scenario. This paper also proposed a modified genetic algorithm to solved both item location assignment and task scheduling with a good computational time result for large size instances within a reasonable amount of time. [ABSTRACT FROM AUTHOR]

The paper sets forth a logical chain of discussions on achieving the goals of the strategic development of freight road transport in the context of digitalization of the economy. The authors analyzed the structure of freight road transport. The paper shows that the solution of this task requires the decomposition of the country's road transport system (RTS), described by a set of parameters, the transformation of which will significantly increase the efficiency indicators of strategic management. The purpose of this paper is to describe the pattern of a unified information system that ensures the effective performance of the functions of strategic management of the development of the RTS, which meets the condition of ensuring the viability of the transport system and increases the roadability at different levels. The paper shows the main components of the information system and considers the intelligent control tools, including a genetic algorithm. The scheme of implementation of the genetic algorithm for the selection of the optimal control effect version within restrictive functions carried out by the subject of control of the macro level of the road transport system is described step by step. [ABSTRACT FROM AUTHOR]

Due to the advanced and hasty development of technology nowadays, researches in relation to Artificial Intelligence (AI) such as Machine Learning (ML), Genetic Algorithm (GA), Neural Network (NN) and Expert System (ES) are progressively introduced. These AI types are also frequently presented in building a new and profitable Algorithmic Trading System (ATS). Apart from Fundamental Analysis (FA), Technical Analysis (TA) based on Technical Indicators (TIs) is one of the most AI used in the development of ATS. However, there is a lack of research despite the available knowledge on TIs in ATS where AI is implemented. Therefore, this study aims to review the ATS current work comprehensively using TIs in AI. A systematic literature review (SLR) has been performed to achieve the objective of this study. This article analyses the rough and systematic literature search process for 17 selected papers from 2010 to 2020. The analysis depicts that in most published studies, GA with multiple TIs is applied. The result of this study leads to a pertinent suggestion that is aimed for further researches on ATS to be conducted by future researchers and traders. [ABSTRACT FROM AUTHOR]

The paper is focused at the phenomenon of heat transport at high temperature and aimes at determination of heat transfer coefficient in particular. The presented research is based on the combination of experimental and computational approach that creates a perfect combination for application of the inverse analysis methods. In this paper, the inverse analysis is conducted by means of genetic algorithm allowing for the identification of heat transfer coefficient as a function of temperature. The results of such an application showed high dependancy of the studied parameter on temperature, especially when temperatures above 500 °C are considered. The outcome of this research underlines the necessity of seeking for the solution in the form of temperature-dependent functions to provide complex and accurate analysis of heat transfer at high temperatures. [ABSTRACT FROM AUTHOR]

Road and traffic problems in Bangladesh are always being barriers to the development of different sectors. This directly impacts on the transportation cost of the industries. An improper transportation system can cause significant damage to any organization. This paper focuses on vehicle routing and travelling cost problem. This paper addresses the routing problem faced by a beverage company in Bangladesh, while delivering products in multiple distant locations in a single trip. The Google sheet and Google map have been used to identify the near optimal solution for the routing by reducing the overall distance of each vehicle. Genetic Algorithm concepts have been used in this paper for cost optimization. The algorithm is constructed to obtain the best feasible solution of optimal number of delivery frequency for rental and organization vehicle with different capacities so that it can minimize the total transportation cost. The optimal route distance travelled by the vehicles was used in the travelling cost optimization problem. The solutions provide clear idea about how optimal routing can be proposed and both types of vehicles are to be selected to get a minimized cost of transportation of the organization. [ABSTRACT FROM AUTHOR]

A genetic algorithm is proposed for finding the optimal distribution of material along the beam length under constraints on a strength and on the maximum value of beam deflection. The paper considers a beam that maintains the section proportions along the entire beam length. I.e. the geometric parameters of the beam cross section are proportional, for example, to its height, which varies along the beam length. The beam is statically determinate, and the load can be arbitrary, including asymmetric and multidirectional. The points (or point) at which the beam deflections are maximal are not known a priori and are being found in the problem solution process. A linear formulation of the problem is considered. The optimization criterion is the mass of the beam. To find beam deflections, i.e. to solve the differential equation for the bending of a beam of variable cross section, the finite difference method is used. The design problem is reduced to the problem of beam height values finding in a system of nodal points. In this case, the desired solution must satisfy the system of constraints on the shift of nodal points and constraints on the maximum stress values in the beam. Shift and stress constraints in each node are considered separately and independently; therefore, the proposed technique allows solving a wide range of problems. An objective function is proposed, this one is the sum of the beam mass and possible penalties for exceeding the maximum deflection specified in the condition and the maximum allowable stress values. During the genetic algorithm work, the beam thicknesses (from present sets) are selected only that ensure the reach of the objective function minimum. A model problem is solved, and it is shown that the proposed algorithm allows one to effectively solve the optimal beams design problems in the presence of constraints on the maximum deflection. The proposed approach can be developed for several design cases, for statically indeterminate constructions, etc. [ABSTRACT FROM AUTHOR]

In the last few years Artificial Intelligence (AI) algorithms and Machine Learning (ML) approaches have been successfully applied in real-world scenarios like commerce, industry and digital services, but they are not a widespread reality in Software Testing. Due to the complexity of software testing, most of the work of AI/ML applied to it is still academic. This paper briefly presents the state of the art in the field of software testing, applying ML approaches and AI algorithms. The progress analysis of the AI and ML methods used for this purpose during the last three years is based on the Scopus Elsevier, web of Science and Google Scholar databases. Algorithms used in software testing have been grouped by test types. The paper also tries to create relations between the main AI approaches and which type of tests they are applied to, in particular white-box, grey-box and black-box software testing types. We conclude that black-box testing is, by far, the preferred method of software testing, when AI is applied, and all three methods of ML (supervised, unsupervised and reinforcement) are commonly used in black-box testing being the “clustering” technique, Artificial Neural Networks and Genetic Algorithms applied to “fuzzing” and regression testing. [ABSTRACT FROM AUTHOR]

Water is essential for all human activities, and that rainfall is one of the critical sources of this precious commodity. Prediction of how much rainfall and when it is most likely to occur will assist concerned officials in planning for its storage and subsequent distribution. Meteorological agencies predict rainfall using statistical or dynamic models. Because of the complexity involved in rainfall prediction and limitations of existing techniques, prediction skill improvement is necessary. Recently, researchers in prediction are using intelligent systems such as Artificial Neural Networks, Fuzzy Inference Systems, Support Vector Machines, and Genetic Algorithms. Many network parameters are required to be selected for the use of these systems, and the choice of the parameters affects the accuracy of the model. Experimental discovery of the parameters is one way, and the other way is to use optimization algorithms. In this paper, various optimization techniques used in computationally intelligent systems are surveyed for rainfall prediction. The optimization techniques mainly used for this purpose are Particle Swarm Optimization, Genetic Algorithm, and Ant Colony Optimization. In all the research articles case study of a certain geographical area for rainfall prediction with a different set of inputs and different forecasting lead times is presented, and hence comparison between the models is difficult. For rainfall prediction, model input selection is equally important to the selection of model parameters as a set of predictors change with increasing or decreasing geographical area and forecast lead time. This paper attempts to identify optimization techniques suitable for medium-range rainfall forecasting over a small geographical area. [ABSTRACT FROM AUTHOR]

In this paper, we propose using the wave finite element method and genetic algorithms to design periodic waveguide structures with optimal stop-bands. Instead of modelling waveguides using the standard finite element method, we use the wave finite element method to model the waveguides. The wave and finite element method is based on using periodic structure theory and the finite element model of a unit cell of the waveguide to describe the waveguide's motion in terms of wave amplitudes rather than in terms of physical degrees of freedom. This results in a numerically efficient model of the waveguide which can be used in optimisation studies. In particular, the focus of this paper is on the optimisation of stop-bands in the waveguides which leverage this property of periodic structures. Genetic algorithms will be used to optimise the stop-bands. A numerical example where analytical models can be obtained will be used to demonstrate the efficacy of the proposed approach and its potential. [ABSTRACT FROM AUTHOR]

Radial basis function neural network (RBFNN) is an artificial feedforward neural network that uses radial basis functions as activation functions in the hidden layer. The output of the RBFNN is a linear combination of the outputs of the hidden layer. This paper presents a multi-objective model of radial basis function neural networks for training. This model aims to satisfy two objectives: the first is the sum of all distances between the input vector and the corresponding center for the selected neurons in the hidden layer and the second is the overall error of the RBFNN, which is defined as the error between the computed output and the expected output. To solve this model, we will use an approach based on genetic algorithms, which allows us to determine an appropriate partitioning of the input data and the optimal weight of the output layer that gives us a good generalization. Numerical results show the performance of the theoretical results presented in this paper, as well as the advantages of the new modeling. [ABSTRACT FROM AUTHOR]

Vehicle routing problems deal with determining the routing of a fleet of vehicles under a set of constraints to serve geographically dispersed customers. The problems belong to a subset of combinatorial optimization problems and are widely studied due to their academic appeal and numerous applications. The classical version of this subject usually minimizes a single objective in total distance or total cost. However, given that many real-life problems are inherently multi-objective, a variant that considers multiple objectives is getting more attention nowadays. The latest review in this area was in 2008 and more than a decade has now elapsed with the absence of similar study. The objectives of this paper are to summarize the selected new research of multi-objective vehicle routing problems that span beyond 2008 and to propose a taxonomy that can be used to categorize the studies in this area. The proposed taxonomy includes eight criteria covering identification and characteristics of the papers. The findings from the review suggest tendencies toward certain scopes such as time windows formulation (VRPTW), the development of population-based algorithms especially the genetic algorithm and elitist non-dominated sorting genetic algorithm (NSGA-II), and the use of Solomon benchmark instances in the numerical experiment. [ABSTRACT FROM AUTHOR]

This paper is focused on travelling salesman problem (TSP) and the impact of genetic operators such as crossover and mutation of Genetic Algorithm (GA). GA is a heuristic technique and is inspired by biological changes. Its performance is compared on different coding platform for different values of different genetic operators. The GA incorporates a few parameters that ought to be balanced, to get dependable outcomes. This paper proposes that for different values of genetic operators of GAs, the optimum value of outcome will be modified appropriately because the GA can adapt its operator's values for a specific problem quickly. Populace evolution or development emerges by using these different genetic operators iteratively and gives a correct solution or a solution with minimum error. This paper provides a study of the impact of different operators on the performance of GA for the optimized solution of TSP. All experiments conducted on python, C and Ruby for the solution of TSP and significant plots generated are useful for researchers. [ABSTRACT FROM AUTHOR]

This paper presents an improved approach to minimize tool path length in multi-tool drilling operations using a genetic algorithm. Hole drilling is prevalent in manufacturing processes, particularly in computer numerical control machines. Holes often vary in size, necessitating tool switches during drilling. As the number of holes and tools increases, finding the optimal path becomes an NP-hard problem. The proposed approach employs genetic algorithms to optimize the tool path for both multi-tool and single-tool drilling. A comparison of the proposed method with existing techniques reveals a reduction of up to 17% in total path length, highlighting the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

There are many ways to extract useful information from biomedical signals. Information theory-based parameters as entropies are strongly related to signal complexity. Measuring the complexity of a biomedical signal as Heart rate Variability (HRV)might be used as a diagnostic tool in medical investigation. This paper presents a theoretical study about HRVsignal analysis using entropymetrics to estimate the signals’ complexity. This study uses multiscale and permutational entropies to achieve the proposed goal. The main purpose of this study is to find a relation between signal complexity and interpretable nonlinear features of HRV. Usually, healthy heart exhibits spatial and temporal complexity, disease can assume a decrease of complexity. The proposed procedures are applied to test signals obtained from specific databases. [ABSTRACT FROM AUTHOR]

The purpose of intrusion detection systems is to improve software and system security. The challenge of attack detection has already been addressed through supervised and unsupervised machine learning approaches. Although the previous methods lead to models that are very accurate on the observed samples, the new methods provide robust models on the unobserved samples. The accuracy of the new methods is equal to the accuracy of the observed samples. In this paper, we apply a deep neural network-based auto-encoder to the GA-AE-IDS (Automatic Encryption Detection System-Genetic Algorithm) intrusion detection system. Basically, the GA-AE-IDS is a light intrusion detection system that can be used online. In this paper, weighted datasets are processed in GA. First, auto-encoder neural networks are initialized; then GA-based weight optimization is performed on the system. The experimental results reflect that the proposed method can detect most attacks with good accuracy. It has also accelerated the training and testing process, and in dense samples has improved by about 4.9% compared to the base paper. Also, the AUC criterion in dense and sparse samples obtained from the proposed method has been improved by 5 and 8%, respectively. [ABSTRACT FROM AUTHOR]

There has been a lot of research in recent years into employing optimization approaches to improve artificial intelligence (AI). In this research review paper, we have compared and contrast some of the most usual optimization algorithms, such as Backtracking searching method (BSA), the genetic algorithm (GA), particle swarm optimization (PSO), an artificial bee colony (ABC), and the genetic algorithm, which are all artificial neural networks (ANNs)-based algorithms. The number of recently developed optimization techniques, such as the lightning search algorithm (LSA) and the whale optimization algorithm (WOA) are also compared. All the techniques are categorized based on randomly generated populations. To produce the best possible results, the processing parameters are set within a certain range according to their knowledge. This review paper emphasis on applying optimization techniques to improve the accuracy of simply adjusting the parameters of the neural network. This review paper also presents some results for enhancing neural network performance using various optimization techniques like PSO, GA, and ABC optimization methods to get optimal processing parameters, such as the number of hidden layers, neurons and learning rate etc. The findings of this research review paper will aid in the improvement in the quality of plastic injection molded parts. [ABSTRACT FROM AUTHOR]

Software quality has always been challenging for both users and software developers. To assure the quality of any software the main focus is on Software Testing, which requires the consumption of resources both quantitatively and qualitatively for any software development firm. The main challenge for any software test analyst is to develop test cases and test data so that maximum software coverage is obtained in minimum test cases. This paper discusses the process of automating test case generation and selection of relevant test data which significantly affects reduction in the cost of the testing process and ensures maximum coverage of software by generating set of key paths covering every part of a test program. During the current research, it is explored that automated software testing is efficient than manual testing because of the use of heuristic search. This paper provides a deep insight of different tools and techniques utilizing Genetic Algorithm (GA) for automating test cases and selecting test data for making the testing process efficient. Along with this, it also focuses on challenges related to this technique, which will provide scope for researchers to elaborate on their research in this area. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of efficient modern control technique for the power split hybrid electric vehicle PSHEV. This technique depends on a genetic algorithm which is used to obtain the optimum torque management between internal combustion engine ICE and electric motor EM. Also, a modified torque strategy approach based on a driving cycle behaviour is developed. The aim of this study is achieved by development of a PSHEV model in Matlab/Simulink. This model consists of ICE, DC generator and DC motor. All power components connected through a power split unit. In addition, a modified configuration of continuously variable transmission CVT is presented which is used to push the engine works near its optimal performance torque. Finally, the controller is tested over two different driving cycles including urban dynamometer driving schedule UDDS and Basrah city canter driving cycle BCCDC. The simulation results show that the effectiveness of control system to keep to obtained minimum fuel consumption, maintain the battery state of charge SOC within the target limit, also control the ICE's operating points within the optimum efficiency maps and exploiting of DC generator machine for capturing maximum braking power. The proposed control system must guarantee the best vehicle performance, especially in fuel economy. [ABSTRACT FROM AUTHOR]

Genetic algorithm (GA) is a popular metaheuristic with wide-ranging applications, e.g. in routing problems such as traveling salesman problem (TSP) or vehicle routing problem (VRP). Seeking the best combination of parameters in GA application is the key objective in the line of research involving GA. One possible factor to be tested is the operator used for crossover. For VRP, a number of research reporting good performance use the order crossover (OX) operator. For TSP, one paper proposed the modified cycle crossover (CX2) operator and reported that it is better than OX and the partially mapped crossover (PMX). The interest and objective of this paper is to test these three operators in a VRP setting. Excluding the crossover operator, other good principles of GA for VRP obtained from the literature are maintained. The experiment results suggest these findings. Firstly, CX2 is expensive in run time and has difficulty escaping from local optimum but leads to the best fitness value compared to the other operators. Secondly, PMX ranks second both in the fitness performance and run time. Thirdly, while OX has slightly inferior performance, it is able to explore wider search space and therefore still has lots of potential for future research. [ABSTRACT FROM AUTHOR]

Dc motors represent linear systems up to point of saturation. In this paper, optimized tuning of DC motors has been discussed with the help of different meta-heuristic algorithms. The model of the DC motor is basically a third-order system. Dc motors, that are used in different industrial applications including conveyors, turntables, and other places where adjustable speed and constant or low-speed torques are required, owing to their simple configuration. They also find its application in dynamic braking and reversing applications as well. Here, in this paper Genetic Algorithm, Differential Evolution, Teaching Learning Based Optimization, Particle Swarm Optimization with different performance indices (Mean Square Error and Integral time absolute error) is compared with the standard Ziegler & Nichols method. Comparison of results using standard step parameters i.e., maximum overshoot, steady-state, rise time and peak time, etc. is being discussed. [ABSTRACT FROM AUTHOR]

This paper presents the cumulative analysis of the different research work regarding the design and modeling of compliant mechanisms for multi DOF. Several prototypes have been manufactured by different authors with proper design, modeling and evaluate its performance through numerical tool or investigate using experimental results. While considering the sliding mode fuzzy disturbance observer (SMFDO) controller, it is very well used for robustness and for tracking performance. Unfortunately relying completely on the flexures or the compliant mechanism is difficult if and only if we require deep knowledge to design and analysis of these parts. Using the appropriate multi-objective genetic algorithm; it's been observed that the design optimization of the mechanism is achieved. In different applications like compliant diaphragm; using the topology optimization approach the problems have been solved and increase the efficiency of the application. Mechanisms like force amplification, mechanical and geometrical advantage; this topology optimization method is best suited. The topology optimization method is one of the popular and general methods for design and develops the application which works on the survival of the fittest strategy. Thereafter validate the results with experimentation and compare them with analytical results. In some of the cases, the combination of static and dynamics for a large class serial-parallel configuration approach is implemented. All these types of compliant mechanisms have the mobility to transform or transmit the force, motion, energy to undergo an elastic deformation without any joints or links. Among all the research work a cumulative conclusion, as well as the future scope for the compliant mechanism for precision applications has been generated which discussed at the end of the paper. [ABSTRACT FROM AUTHOR]

The traditional BP neural network training method processes the training dataset serially on one machine, so the efficiency is quite low. The massive data that need to be explored brings great challenge for BP neural network. The traditional serial training method of BP neural network will encounter many problems, such as costing too much time and insufficient memory to finish the training process. To solve these problems, this paper proposes a new parallel training method that is based on MapReduce and genetic algorithm, and the new training method is called MR-GAIBP (MapReduce based Genetic Algorithm Improved Back Propagation). MR-GAIBP includes two parts: MapReduce based BP algorithm and MapReduce based genetic algorithm. Genetic algorithm is first iterated for a few times to find appropriate initial weights of BP neural network, then BP algorithm is used to find the appropriate weights that meets the requirement. In the phase of BP algorithm, local iteration is used to speed up the convergence. Experiment results demonstrate that MR-GAIBP has faster convergence rate and higher accuracy compared with the previous MapReduce based algorithm proposed in other papers. [ABSTRACT FROM PUBLISHER]

Combined cycle power plants are often used to produce power. These days prediction of power plant output based on operating parameters is a major concern. This paper presents an approach to using computational intelligence technique to predict the output power of combined cycle power plant. Computational intelligence techniques have been developed and applied to many real world problems. In this paper, tree architectures of fuzzy neural networks are considered to predict the output power. Tree architectures of fuzzy neural networks have an advantage of reducing the number of rules by selecting fuzzy neurons as nodes and relevant inputs as leaves optimally. For the optimization of the networks, two-step optimization method is used. Genetic algorithms optimize the binary structure of the networks by selecting the nodes and leaves as binary, and followed by random signal-based learning further refines the optimized binary connections in the unit interval. To verify the effectiveness of the proposed method, combined cycle power plant dataset obtained from the UCI Machine Learning Repository Database is [ABSTRACT FROM AUTHOR]

In order to improve the working efficiency of RGV-CNC intelligent processing system, this paper constructs a model with predictive control and rolling optimization function from shallow to deep. The model can be applied to one process and two processes, the rolling optimization model also has excellent stability in the face of possible failures. For the establishment of the scheduling model of RGV cars, this paper first conducted a comprehensive analogy between the abstract problem and the actual disk scheduling, and found that the SPSS algorithm in the disk scheduling algorithm can adapt to this environment well. On this basis, this paper conducts an in-depth study on the "request-response" mechanism between RGV and CNC and summarizes the general rules. It is found that the working mechanism of the system is very similar to the "autocorrelation/cross-correlation influence" that is common in signal analysis. Based on this, this paper constructed the self-owned and mutual influence complementary RVG scheduling strategy, and used genetic algorithm to optimize the overall situation. The results showed that under the scheduling strategy constructed in this paper, the average utilization rate of CNC was as high as 95%, which strongly proved the scientific and practical nature of this model [ABSTRACT FROM AUTHOR]

IoT and its effect in sharing information has been a huge mutation for Internet; increasing development and use of internet in healthcare departments requires more optimized performance in the speed of sharing and publishing information and energy consumption. An important aspect in this field is routing optimization in IoT; we introduce a new idea, which is a combination of two algorithms: PSO optimization algorithm (particle swarm optimization) and GA algorithm (genetic algorithm) and it is used for routing optimization and latency reduction during sending information between IoT groups in an IoT healthcare system. [ABSTRACT FROM AUTHOR]

This paper presents an analysis study of three penalty function methods used in binary genetic algorithm for solving beam-slab layout problem. The beam-slab layout problem is the design problem for floor plan with predefined wall positions and column positions. Design of beam-slab layout is considered to be a heuristic task as it cannot be solved by algorithms directly. Firstly, this design problem is represented as an optimization problem by using necessary objective function and constraint equations formulated from structural engineering considerations. In this study a binary genetic algorithm is implemented with three types of penalty function methods (Static penalty function, Dynamic penalty function and Adaptive penalty function) for solving the representative optimization problem. The analysis of genetic algorithm and penalty functions has been done evaluating these on different architectural floor plans. [ABSTRACT FROM AUTHOR]

Genetic Algorithms in the design of microheaters for gas sensors has become increasingly popular in recent years. This is due to their versatility and ability to perform an extensive search in complex multimodal search places. This paper studies about utilizing a genetic algorithm (GA) based software with a complete structural design and thermos-electromechanical and transient analysis using Intellisuite v8.2 s/w to optimize the physical design parameters of a microheater of a MEMS based gas sensor and to get the desired temperature (∼200oC) at minimum power consumption (∼140mW) within desired physical constraints. [ABSTRACT FROM AUTHOR]

Efficiency is affected by the geometry parameter and rotor slot number selection therefore a Genetic Algorithm based optimal design of a three-phase squirrel cage induction motor is applied to improve the efficiency of IM 2.2kW-4P from efficiency class IE2 to IE 4 motor design. An analytical calculation will investigate power, losses, and efficiency with different geometrical parameters of stator/rotor. Different constraints and different variables are imposed to achieve the best design within a specified range of variables. A genetic algorithm is used to achieve optimal design of the Squirrel Cage Induction motor-SCIM 2.2kW-4P with 36 stator slots/28,32, 40, and 44 rotor bars are verified under starting and constant speed. Their electromagnetic characteristics, such as electromagnetic torque, stator current, and magnetic flux density are compared in between two configurations. The paper contributes that the proper geometry parameters have a strong impact on the induction motor efficiency and the best design is applied for a 2.2kW induction motor with fixed stator and rotor diameters. The results obtained after running the optimization technique give visible improvements in efficiency as well as cost. To obtain the best design efficiency and cost of material is obtained using multi-objective Genetic Algorithm. [ABSTRACT FROM AUTHOR]

Portfolio optimization aims to protect investors against any risks which they may experience. Stock diversification is one of the solutions to optimize stock portfolio, where a diverse portfolio tends to have less risk than the undiversified one. Agglomerative clustering is a hierarchical clustering method. Applying diversification concept, agglomerative clustering is used to cluster 40 different assets based on their financial ratio scores (Current Ratio, Debt-Equity Ratio, Profit Margin, Return on Equity, Price/Earnings per Growth, EPS diluted, and Price/Earnings Ratio). Genetic algorithm is search method based on principles of natural selection and genetics. After the stocks are clustered, Genetic algorithm with heuristic crossover is applied to each cluster alongside to determine the proportion of each stock. In this paper, a possibilistic mean-semi-absolute deviation optimization model is used where cardinality, quantity, and transaction cost are considered as constraints. We also use the assumption that the returns of risky assets are fuzzy numbers. The implementation shows that the method gave a higher level of return (29.77 %) and Sharpe’s ratio (18.71) compared to S&P 500 index in the same period of time (12.34 % and 2.7 respectively). [ABSTRACT FROM AUTHOR]

Cancer is one of the most deadly diseases for humans. According to the WHO (2015), cancer is the causes of the death number two in the world by 13 % after cardiovascular disease. Cancer often causes death if treatment is too late. Therefore, early detection of cancer is necessary to avoid the spread of cancer. High-dimensional medical data is one of the obstacles to the application of machine learning techniques due to a negative effect on the process of analysis. Therefore, the selection features required to increase performance in the detection of cancer. This paper focuses on the comparison of feature selection on cancer data. We use Genetic Algorithm and Laplacian Score for cancer gene selection of features, coupled with the Support Vectors Machines for cancer classification. The results will show that Genetic Algorithm gives the best accuracy with the percentage of 98.69 % only using 40 features. [ABSTRACT FROM AUTHOR]

GA-based regression test prioritization have ordered test cases by computing fitness value based on the number of affected faults in the coverage information, but most of the researchers use the same severity of faults even if a fault was executed by the previous test case. There have been very little evaluations of the GA-based regression test prioritization, even though there are several studies on GA-based regression test prioritization of object-oriented program (OOP). Most of the evaluations of the previous studies do not consider fault detection efficiency in terms of mutation scores and execution efficiency in terms of execution effort but consider only Average Percentage of the rate of Fault Detection (APFD) metric. The objective of this paper is to integrate the idea of GA with object-oriented programs to aid automated regression test case prioritization of the selected test cases, by proposing a regression test case prioritization strategy for selected test cases of object-oriented programs based on genetic algorithm for efficient OOP regression test case prioritization. This paper proposed an automatic test case prioritization strategy, called HoceDanMafara, and its tool support for Object-Oriented programs. Moreover, a comprehensive empirical study of ten object-oriented programs by the use of mutation analysis was conducted to compare HoceDanMafara and one existing software regression tests prioritization together with non-prioritize and random strategies for regression testing of OOP in term of efficiency of fault detection. The evidence of the efficiency of the proposed strategy are shown in the results of the experiment and statistical tests (p<0.05). The study indicated that the resulting evolutionary tests prioritization produces 27.75% in terms of test effort efficiency compare with randPrior that produces 20.93%, nonPrior produces 14.35% and pSherry produces 20.89%. Therefore, the proposed strategy could be commendable of use as an efficient OOP automatic tests prioritization strategy. [ABSTRACT FROM AUTHOR]

In this paper a optimization procedure for the design of geometrical configuration of acoustic bricks is presented. The optimization process combines both computational simulation approach together with genetic algorithm. However, for the successful solution several necessary tasks must be accomplished. First, the selection and implementation of proper acoustic model is needed and second, the method for generation of geometrical configuration for the solution by Finite Element Method must be found. Therefore, the paper does not only describe the optimization procedure with recent knowledge and achievements, but also proposes several future objectives necessary for successful solution of the optimization problem. [ABSTRACT FROM AUTHOR]

To minimize makespan for scheduling independent tasks in cloud computing, an improved estimation of distribution algorithm (IEDA) is proposed to tackle the investigated problem in this paper. Considering that the problem is concerned with multi-dimensional discrete problems, an improved population-based incremental learning (PBIL) algorithm is applied, which the parameter for each component is independent with other components in PBIL. In order to improve the performance of PBIL, on the one hand, the integer encoding scheme is used and the method of probability calculation of PBIL is improved by using the task average processing time; on the other hand, an effective adaptive learning rate function that related to the number of iterations is constructed to trade off the exploration and exploitation of IEDA. In addition, both enhanced Max-Min and Min-Min algorithms are properly introduced to form two initial individuals. In the proposed IEDA, an improved genetic algorithm (IGA) is applied to generate partial initial population by evolving two initial individuals and the rest of initial individuals are generated at random. Finally, the sampling process is divided into two parts including sampling by probabilistic model and IGA respectively. The experiment results show that the proposed IEDA not only gets better solution, but also has faster convergence speed. [ABSTRACT FROM AUTHOR]

In this paper, a new multi-agent genetic algorithm based on orthogonal experiment is proposed, which is based on multi-agent system, genetic algorithm and orthogonal experimental design. The design of neighborhood competition operator, orthogonal crossover operator, Son and self-learning operator. The new algorithm is applied to mobile tower defense game, according to the characteristics of the game, the establishment of mathematical models, and finally increases the value of the game’s monster. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for the enhancement of a Brush Less Direct Current motor (BLDC) with 6Poles and 8slots. In particular; it is focused on amulti-objective optimization using a Genetic Algorithmand Grey Wolf Optimization developed in MATLAB. The optimization aims to maximize the maximum output power value and minimize the total losses of a motor. This paper presents an application of the MATLAB optimization algorithms to brushless DC (BLDC) motor design, with 7 design parameters chosen to be free. The optimal design parameters of the motor derived by GA are compared with those obtained by Grey Wolf Optimization technique. A comparative report on the specified enhancement approaches appearsthat Grey Wolf Optimization technique has a better convergence. [ABSTRACT FROM AUTHOR]

Hybridization is a common theme employed to improve metaheuristics. Having been known as a less effective metaheuristic for the vehicle routing problem, genetic algorithm (GA) has received attention from researchers for modification and improvement by means of hybridization, for example by adopting a local search technique for the mutation operator. In this paper, we propose another hybridization idea by using an adaptive threshold in population management, whereby in earlier stages of the GA iterations, a larger threshold is used to open up the search space, and in later stages the threshold will be reduced to intensify the search in a smaller neighborhood area. This idea is similar to diversification and intensification processes used in the Tabu Search. The main GA engine follows good principles found from the literature. Two crossover operators, the partially mapped crossover (PMX) and the order crossover (OX), were also tested to see if the adaptive threshold has complication with the other concepts. The experiment results based on Solomon benchmark instance point out that the adaptive threshold favors the PMX but produces worse fitness and longer run time with the OX. More fine-tuning of parameters is invited to further enhance the GA performance from this research. [ABSTRACT FROM AUTHOR]

Many industrial companies continue to face global uncertainties in demand and failures in supply. The main purpose of this research is to design and optimize a supply chain network (SCN) that performs under completely uncertain environment. In this paper, three advanced meta-heuristic algorithms based on Broyden-Fletcher-Goldfarb-Shanno (BFGS), POWELL, and Non-dominated Sorting Genetic Algorithm (NSGA-II) are used to solve the optimization problem. A real-life case study for a steel manufacturing integrated supply chain is used to demonstrate the efficiency of the model and the solutions obtained by meta-heuristic algorithms. The objective was to maximize the total profit of supply chain network under disruption conditions. The presented mathematical modeling provides an understandable overview of the system for managers to make appropriate decisions to achieve the maximum profit. Findings revealed that advanced meta-heuristic algorithms were the most efficient technique to solve the proposed model when compared with the traditional method. [ABSTRACT FROM AUTHOR]

Cybersecurity utilizes Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHAs) to distinguish malware from people. This paper proposes an Arabic-character CAPTCHA creation approach to improve security and inclusivity. Differential evolution and genetic algorithms optimize CAPTCHAs of various complexity. The technique builds a recursive population of alternative solutions using DEAP (Distributed Evolutionary Algorithms in Python) to increase CAPTCHA similarity to the intended text. Arabic letters enhance a character set, making computerized solvers harder and supporting Arabic-speaking users. The approach delivers robust and diversified CAPTCHAs, according to tests. A multi-algorithmic approach to multilingual CAPTCHA usability and security appears promising. [ABSTRACT FROM AUTHOR]

Nanotechnology invades all fields of the science to become the focus of attention of the researchers over the entire wild world. This technique allows the controlling of the atom and being able to move it freely and easily within an element or compound. Upon these operations, the properties of materials may change and it is possible to obtain new compounds that cannot be obtained by the available scientific methods. It also focuses on the processes of separating, combining, and reforming materials with a single atom or part. Artificial intelligence applications have played an important role in accelerating the space of nanoscale measurements of elements and various algorithms have been proposed in order to improve that. The neural networks, which are used for image recognition, fuzzy logic which is used for prediction and the Genetic Algorithm (GA) for optimization are the most known one. In addition, GA has the ability to improve a set of solutions in order to obtain the optimal one according to tuning of the specific parameters. This paper aims to propose an algorithm based on the original GA to enable reconfiguration of the particles of an element in order to obtain the largest possible space between the particles or atoms of the elements. The available space within an atomic element or molecule is estimated in nanometers. This gives the opportunity for the possibility of proper exploitation of such spaces in more clear and accurate industries manners according to the properties of the resulting material. [ABSTRACT FROM AUTHOR]

The travelling salesperson problem (TSP) is the most studied problem in optimization and computer science. A more challenging version of the Travelling Salesperson Problem (TSP) is the Multiple Travelling Salesperson Problem (MTSP). The goal is to find the shortest path, which is the shortest distance for each salesperson to travel from the depot to each city and return to depot. It is a NP hard problem and has various applications in routing and scheduling. There are various algorithms to solve MTSP such as exact solution method, bioinspired algorithms like genetic ant colony, evolutionary. In this paper we review exact algorithm and bioinspired algorithm. [ABSTRACT FROM AUTHOR]

Educational institutions frequently struggle to develop a workable lecture/tutorial schedule in a major universitydepartment. In this study, an evolutionary algorithm (EA)-based method is used to resolve the university scheduling problem. Throughout the process, a chromosomal representation tailored to the task is used. In order to generate workable timetables in a fair amount of computation time, heuristics and context-based reasoning have been applied. The strategy used to accelerate convergence is clever adaptive mutation. Real data from a large university has been used to validate, test, and debate the whole course scheduling system mentioned in this paper. Higher education institutions are currently concerned about students' attendance patterns. For the faculty to construct schedules automatically, a computer-based automatic timetable generator system is required. Our ideas for the "Automatic Time Table System," which consists of many applications, have been laid out. The application's timetable generation feature helps you save time. The system's automated Excel spreadsheet is utilized to maintain a record of the teachers' availability and scheduling. Our students will find this system helpful in the twenty-first century. It takes a lot of time and labor for educational staff in universities with huge student numbers to manually create a schedule. Due to this, it commonly happens that the same professor will give numerous lectures or competing classes. Automatically generating test and class schedules will be made simpler by the availability of a timetable generator. It will be produced by the system automatically, which will also help you save time. It prevents having to manually set up and manage a schedule. The purpose of this project is to develop a simple, functional, and user-friendly application that will facilitate the construction and distribution of schedules. The genetic algorithm is themain technique used to make schedules. It helps in the development of the ideal schedule by regulating all the rules. Conflicts in scheduling are not an issue for the faculty. With inputs such teacher name, data for the rooms, labs, and subject, this system will have a user-friendly, interactive, and less complicated interface. To store all the data entered as input, the system will have a carefully constructed database. The system will have an algorithm that manages all of the database's data while taking into account both hard and soft constraints. a timetable generation technique that seeks out the optimal solution using genetic algorithms. Instead of requiring time-consuming documents, the staff and students can easily view the timetable. [ABSTRACT FROM AUTHOR]

Search-based data analytics is a big area that requires great attention in many different areas. To address real-time issues, many methods and algorithms are utilised for search-based optimization. Many fields of study include design, accounting, finance, continuous imaging, and many more, but the most important are machine learning and deep learning. The experts are experimenting with a wide range of technologies and advancements, including open source, and have developed methods that enable them to achieve a greater degree of precision. When it comes to deep learning, it's inextricably linked to machine learning since it combines greater levels of execution and accuracy with a lower base error rate. Genetic Method, a high-performance algorithm for engineering optimization, is presented in the paper as use cases. For the purposes of data analytics and in particular for a multiprocessor scheduling strategy, the goal of this article is to provide the effective implementation of the famous evolutionary computing technique of genetic algorithm for genetic algorithms. Due to its better results on a variety of factors, the genetic algorithm described here may offer real and measurable results. There were no successful outcomes from the prior techniques' usage of greedy-based approaches, therefore researchers turned to genetic algorithm-based optimization. To design and simulate the implementation scenarios, we used MATLAB and Java-based development tools, and the results were very useful for optimizing. [ABSTRACT FROM AUTHOR]

Today, Maintaining secure, reliable and secure communication of information between different organizations is very important. However, secure data communications are always vulnerable to intruders and misuse over the Internet and other networks. To achieve this, intrusion detection systems have become a necessary component of computer and computer network security. However, different approaches are used for intrusion detection. Unfortunately, not all previous systems are completely error-free. Therefore, try to improve security. In this course, we will introduce the intrusion detection system "IDS". Before applying a genetic algorithm (GA) to efficiently detect different types of network intruders. Use parameters to learn and implement the GA evolution process. Because of the ascent in specialized progressions, there is likewise an unexpected spike in cyber attacks. To safeguard against these dangers, the IDS is a viable technique, however the standard IDS isn't exceptionally astute and strong to hold the client back from experiencing new assaults on an ordinary premise. To improve the classifiers and calculations, AI classifiers and calculations can be utilized. These AI models are extremely useful and can prepare models to recognizetypical traffic and awful traffic. By means of the help of AI, IDS can then perceive inconsistency assaults and stay away from them. With standard IDS, while distinguishing oddity assaults, the bogus positive rate is high, and that implies that it is erroneous, to limit the misleading positive rate ML calculations can be utilized. Additionally, oddity recognition is conceivable involving ML in interruption identification which will give a high exactness of assaults being distinguished. Our proposed paper includes various ML Algorithm to filter and reduce traffic data complicated. [ABSTRACT FROM AUTHOR]

This paper researches on e-government website evaluation. After establishing the evaluation index system, this paper reduces the evaluation index system by rough set. Then, this paper introduces genetic algorithm which are optimized to BP neural network weights and thresholds, and establishes e-government website evaluation model based on genetic neural network algorithm. It is exemplified that the evaluation result is reasonable, and the evaluation model provides a new way of thinking for evaluation on egovernment websites. [ABSTRACT FROM AUTHOR]